{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7e6d454c",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/nyandwi/machine_learning_complete/blob/main/6_classical_machine_learning_with_scikit-learn/3_support_vector_machines_for_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "651045d9",
   "metadata": {},
   "source": [
    "*This notebook was created by [Jean de Dieu Nyandwi](https://twitter.com/jeande_d) for the love of machine learning community. For any feedback, errors or suggestion, he can be reached on email (johnjw7084 at gmail dot com), [Twitter](https://twitter.com/jeande_d), or [LinkedIn](https://linkedin.com/in/nyandwi).*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c5ba4fe",
   "metadata": {},
   "source": [
    "<a name='0'></a>\n",
    "# Support Vector Machines (SVM) - Intro and SVM for Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a262086",
   "metadata": {},
   "source": [
    "Support Vector Machines are the type of supervised learning algorithms used for regression, classification and detecting outliers. SVMs are remarkably one of the powerful models in classical machine learning suited for handling complex and high dimensional datasets. \n",
    "\n",
    "With SVM supporting different kernels (linear, polynomial, Radial Basis Function(rbf), and sigmoid), SVM can tackle different kinds of datasets, both linear and non linear. \n",
    "\n",
    "While the maths behind the SVMs are beyond the scope of this notebook, here is the idea behind SVMs:\n",
    "\n",
    "*The way SVM works can be compared to a street with a boundary line. During SVM training, SMV draws the large margin or decision boundary between classes based on the importance of each training data point. The training data points that are inside the decision boundary are called support vectors and hence the name.*\n",
    "\n",
    "![SVM](https://upload.wikimedia.org/wikipedia/commons/thumb/f/fe/Kernel_Machine.svg/1200px-Kernel_Machine.svg.png)\n",
    "\n",
    "SMVs are widely used for classification. But to motivate that, let's start with regression. For the purpose of examining how powerful this algorithm is, we will use the same dataset that we used in linear regression notebook, and hopefully the difference in performance will be notable. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5e8372",
   "metadata": {},
   "source": [
    "### Contents\n",
    "\n",
    "* [1 - Imports](#1)\n",
    "* [2 - Loading the data](#2)\n",
    "* [3 - Exploratory Analysis](#3)\n",
    "* [4 - Preprocessing the data](#4)\n",
    "* [5 - Training Support Vector Regressor](#5)\n",
    "* [6 - Evaluating Support Vector Regressor](#6)\n",
    "* [7 - Improving Support Vector Regressor](#7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99058d12",
   "metadata": {},
   "source": [
    "<a name='1'></a>\n",
    "## 1 - Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "df278c5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import urllib.request\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35699c38",
   "metadata": {},
   "source": [
    "<a name='2'></a>\n",
    "\n",
    "## 2 - Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "51f9f17f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'https://raw.githubusercontent.com/nyandwi/public_datasets/master/housing.csv'\n",
    "\n",
    "# This require internet \n",
    "\n",
    "def download_read_data(path):\n",
    "    \n",
    "    \"\"\"\n",
    "     Function to retrive data from the data paths\n",
    "     And to read the data as a pandas dataframe\n",
    "  \n",
    "    To return the dataframe\n",
    "    \"\"\" \n",
    "    \n",
    "      ## Only retrieve the directory of the data\n",
    "\n",
    "    data_path =  urllib.request.urlretrieve(path)[0]\n",
    "    data = pd.read_csv(path)\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10e640a0",
   "metadata": {},
   "source": [
    "The above function require internet to load retrieve the data. To read the data without internet, download it from the provided link and read it with Pandas read_csv function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d848e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal_data = download_read_data(data_path)\n",
    "cal_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c1fc45c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20640"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cal_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c492fd93",
   "metadata": {},
   "source": [
    "## 3 - Exploratory Analysis\n",
    "\n",
    "This is going to be a quick glance through the dataset. The full exploratory data analysis was done in the linear regression notebook[ADD LINK]. Before anything, let's split the data into training and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "98d74144",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of training data is: 18576 \n",
      "The size of testing data is: 2064\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_data, test_data = train_test_split(cal_data, test_size=0.1,random_state=20)\n",
    "\n",
    "print('The size of training data is: {} \\nThe size of testing data is: {}'.format(len(train_data), len(test_data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0314e577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>18576.0</td>\n",
       "      <td>-119.567530</td>\n",
       "      <td>2.000581</td>\n",
       "      <td>-124.3500</td>\n",
       "      <td>-121.7900</td>\n",
       "      <td>-118.4900</td>\n",
       "      <td>-118.010000</td>\n",
       "      <td>-114.4900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>18576.0</td>\n",
       "      <td>35.630217</td>\n",
       "      <td>2.133260</td>\n",
       "      <td>32.5400</td>\n",
       "      <td>33.9300</td>\n",
       "      <td>34.2600</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>41.9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>18576.0</td>\n",
       "      <td>28.661068</td>\n",
       "      <td>12.604039</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>52.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>18576.0</td>\n",
       "      <td>2631.567453</td>\n",
       "      <td>2169.467450</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1445.0000</td>\n",
       "      <td>2127.0000</td>\n",
       "      <td>3149.000000</td>\n",
       "      <td>39320.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>18390.0</td>\n",
       "      <td>537.344698</td>\n",
       "      <td>417.672864</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>295.0000</td>\n",
       "      <td>435.0000</td>\n",
       "      <td>648.000000</td>\n",
       "      <td>6445.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>18576.0</td>\n",
       "      <td>1422.408376</td>\n",
       "      <td>1105.486111</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>785.7500</td>\n",
       "      <td>1166.0000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>28566.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>18576.0</td>\n",
       "      <td>499.277078</td>\n",
       "      <td>379.473497</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>279.0000</td>\n",
       "      <td>410.0000</td>\n",
       "      <td>606.000000</td>\n",
       "      <td>6082.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>18576.0</td>\n",
       "      <td>3.870053</td>\n",
       "      <td>1.900225</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>2.5643</td>\n",
       "      <td>3.5341</td>\n",
       "      <td>4.742725</td>\n",
       "      <td>15.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>18576.0</td>\n",
       "      <td>206881.011305</td>\n",
       "      <td>115237.605962</td>\n",
       "      <td>14999.0000</td>\n",
       "      <td>120000.0000</td>\n",
       "      <td>179800.0000</td>\n",
       "      <td>264700.000000</td>\n",
       "      <td>500001.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count           mean            std         min  \\\n",
       "longitude           18576.0    -119.567530       2.000581   -124.3500   \n",
       "latitude            18576.0      35.630217       2.133260     32.5400   \n",
       "housing_median_age  18576.0      28.661068      12.604039      1.0000   \n",
       "total_rooms         18576.0    2631.567453    2169.467450      2.0000   \n",
       "total_bedrooms      18390.0     537.344698     417.672864      1.0000   \n",
       "population          18576.0    1422.408376    1105.486111      3.0000   \n",
       "households          18576.0     499.277078     379.473497      1.0000   \n",
       "median_income       18576.0       3.870053       1.900225      0.4999   \n",
       "median_house_value  18576.0  206881.011305  115237.605962  14999.0000   \n",
       "\n",
       "                            25%          50%            75%          max  \n",
       "longitude             -121.7900    -118.4900    -118.010000    -114.4900  \n",
       "latitude                33.9300      34.2600      37.710000      41.9500  \n",
       "housing_median_age      18.0000      29.0000      37.000000      52.0000  \n",
       "total_rooms           1445.0000    2127.0000    3149.000000   39320.0000  \n",
       "total_bedrooms         295.0000     435.0000     648.000000    6445.0000  \n",
       "population             785.7500    1166.0000    1725.000000   28566.0000  \n",
       "households             279.0000     410.0000     606.000000    6082.0000  \n",
       "median_income            2.5643       3.5341       4.742725      15.0001  \n",
       "median_house_value  120000.0000  179800.0000  264700.000000  500001.0000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking statistics\n",
    "\n",
    "train_data.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d6c859d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude               0\n",
       "latitude                0\n",
       "housing_median_age      0\n",
       "total_rooms             0\n",
       "total_bedrooms        186\n",
       "population              0\n",
       "households              0\n",
       "median_income           0\n",
       "median_house_value      0\n",
       "ocean_proximity         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking missing values\n",
    "\n",
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f52edb8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     8231\n",
       "INLAND        5896\n",
       "NEAR OCEAN    2384\n",
       "NEAR BAY      2061\n",
       "ISLAND           4\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking Values in the Categorical Feature(s)\n",
    "\n",
    "train_data['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a5d91ea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ocean_proximity', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=train_data, x='ocean_proximity')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2339e7e",
   "metadata": {},
   "source": [
    "Total bedroom is the only feature having missing values. Here is its distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0bfe514d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zRCRJnRkikqTOxhYiSfZLckuSL7b1hUluSDKV5PIk+7f6AW19qm1f0PcaZ7f6PUmOH9OhSNI+a5xnIm8D7u5bPw84v6qeDzwEnNHqZwAPtfr5bRxJjgJOAV4ELAM+nMRfYpSkERpLiCSZD5wEfKytB3gNcGUbcglwclte3tZp249r45cDl1XVT6rqPmAKOHokByBJAsZ3JvJB4J3Az9r6c4CHq2pbW98IzGvL84D7Adr2R9r4J+ozPEeSNAIjD5EkrwM2V9XNI9znyiTrkqzbsmXLqHYrSb/0xnEm8krg9Uk2AJfRu4z1IWB2klltzHxgU1veBBwJ0LYfAvywvz7Dc56kqi6qqiVVtWTu3Ll79mgkaR828hCpqrOran5VLaB3Y/xrVfUm4DrgDW3YCuCqtrymrdO2f62qqtVPabO3FgKLgBtHdBiSJGDWroeMzLuAy5L8CXALcHGrXwx8IskUsJVe8FBVdya5ArgL2AacWVU/HX3bw7fgrC+Nbd8bzj1pbPuWtPcba4hU1deBr7fle5lhdlVV/SPwezt4/jnAOcPrUJK0M35iXZLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqTNDRJLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqTNDRJLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzkYeIkmOTHJdkruS3Jnkba1+aJK1Sda3v3NaPUkuSDKV5LYki/tea0Ubvz7JilEfiyTt68ZxJrIN+MOqOgo4FjgzyVHAWcC1VbUIuLatA5wALGqPlcCF0AsdYBVwDHA0sGo6eCRJozHyEKmqB6rqm23574G7gXnAcuCSNuwS4OS2vBy4tHquB2YnOQI4HlhbVVur6iFgLbBsdEciSRrrPZEkC4CXAjcAh1fVA23Tg8DhbXkecH/f0za22o7qM+1nZZJ1SdZt2bJlzx2AJO3jxhYiSZ4JfBZ4e1U92r+tqgqoPbWvqrqoqpZU1ZK5c+fuqZeVpH3eWEIkydPpBcgnq+pzrfz9dpmK9ndzq28Cjux7+vxW21FdkjQi45idFeBi4O6q+kDfpjXA9AyrFcBVffXT2iytY4FH2mWva4ClSea0G+pLW02SNCKzxrDPVwL/Drg9ya2t9m7gXOCKJGcA3wXe2LZdDZwITAGPAacDVNXWJO8Hbmrj3ldVW0dyBJIkYAwhUlV/DWQHm4+bYXwBZ+7gtVYDq/dcd5Kkp8JPrEuSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1Nk4fk9EE2TBWV8ay343nHvSWPYr6anxTESS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4mfopvkmXAh4D9gI9V1bljbkl7wLimFoPTi6WnYqLPRJLsB/w5cAJwFHBqkqPG25Uk7Tsm/UzkaGCqqu4FSHIZsBy4a6xdaaKN8yxoXDz7UleTHiLzgPv71jcCx2w/KMlKYGVb/Yck93Tc32HADzo+d9zsfXz2+v5z3g437fW974S971m/NlNx0kNkIFV1EXDR7r5OknVVtWQPtDRy9j4+k9y/vY/HJPU+0fdEgE3AkX3r81tNkjQCkx4iNwGLkixMsj9wCrBmzD1J0j5joi9nVdW2JG8FrqE3xXd1Vd05xF3u9iWxMbL38Znk/u19PCam91TVuHuQJE2oSb+cJUkaI0NEktSZITKAJMuS3JNkKslZ4+5nWpLVSTYnuaOvdmiStUnWt79zWj1JLmjHcFuSxX3PWdHGr0+yYkS9H5nkuiR3Jbkzydsmpf8kBya5Mcm3Wu//rdUXJrmh9Xh5m+xBkgPa+lTbvqDvtc5u9XuSHD/s3vv2u1+SW5J8cZJ6T7Ihye1Jbk2yrtX2+vdM2+fsJFcm+XaSu5O8YlJ636mq8rGTB70b9n8LPA/YH/gWcNS4+2q9vQpYDNzRV/tT4Ky2fBZwXls+EfgyEOBY4IZWPxS4t/2d05bnjKD3I4DFbflZwHfofXXNXt9/6+GZbfnpwA2tpyuAU1r9I8B/astvAT7Slk8BLm/LR7X30wHAwvY+229E7513AJ8CvtjWJ6J3YANw2Ha1vf490/Z7CfAf2vL+wOxJ6X2nxzXOnU/CA3gFcE3f+tnA2ePuq6+fBTw5RO4BjmjLRwD3tOWPAqduPw44FfhoX/1J40Z4HFcBr520/oGDgW/S+6aEHwCztn/f0Js9+Iq2PKuNy/bvpf5xQ+55PnAt8Brgi62XSel9A78YInv9ewY4BLiPNplpknrf1cPLWbs201erzBtTL4M4vKoeaMsPAoe35R0dx9iPr10ieSm9f9FPRP/tctCtwGZgLb1/iT9cVdtm6OOJHtv2R4DnjKt34IPAO4GftfXnMDm9F/CVJDen93VGMBnvmYXAFuAv2mXEjyV5BpPR+04ZIr/EqvdPlb16DneSZwKfBd5eVY/2b9ub+6+qn1bVb9H7V/3RwAvH29FgkrwO2FxVN4+7l45+u6oW0/vm7jOTvKp/4178nplF79LzhVX1UuBH9C5fPWEv7n2nDJFdm7SvVvl+kiMA2t/Nrb6j4xjb8SV5Or0A+WRVfa6VJ6Z/gKp6GLiO3iWg2UmmP8Db38cTPbbthwA/ZDy9vxJ4fZINwGX0Lml9aEJ6p6o2tb+bgc/TC/BJeM9sBDZW1Q1t/Up6oTIJve+UIbJrk/bVKmuA6RkbK+jda5iun9ZmfRwLPNJOo68BliaZ02aGLG21oUoS4GLg7qr6wCT1n2Ruktlt+SB693Luphcmb9hB79PH9Abga+1fnWuAU9oMqIXAIuDGYfZeVWdX1fyqWkDvvfy1qnrTJPSe5BlJnjW9TO9/6zuYgPdMVT0I3J/kBa10HL2frNjre9+lcd6QmZQHvZkS36F33fs94+6nr69PAw8A/0TvXzpn0LtefS2wHvgqcGgbG3o/4PW3wO3Akr7X+QNgqj1OH1Hvv03v1P024Nb2OHES+gdeAtzSer8DeG+rP4/ef0ingM8AB7T6gW19qm1/Xt9rvacd0z3ACSN+/7yan8/O2ut7bz1+qz3unP7/4iS8Z9o+fwtY1943X6A3u2oiet/Zw689kSR15uUsSVJnhogkqTNDRJLUmSEiSerMEJEkdWaISJI6M0QkSZ39f8Alxp+/uWKEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['total_bedrooms'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4968843f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting geographical features\n",
    "\n",
    "plt.figure(figsize=(12,7))\n",
    "\n",
    "sns.scatterplot(data = train_data, x='longitude', y='latitude', hue='ocean_proximity', \n",
    "                size='median_house_value')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6733727",
   "metadata": {},
   "source": [
    "<a name='4'></a>\n",
    "\n",
    "## 4 - Data Preprocessing \n",
    "\n",
    "To do: \n",
    "* Handle missing values\n",
    "* Encode categorical features\n",
    "* Scale the features\n",
    "* Everything into pipeline\n",
    "   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0f099aa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting training input data and labels\n",
    "\n",
    "training_input_data = train_data.drop('median_house_value', axis=1)\n",
    "training_labels = train_data['median_house_value']\n",
    "\n",
    "# Numerical features \n",
    "\n",
    "num_feats = training_input_data.drop('ocean_proximity', axis=1)\n",
    "\n",
    "# Categorical features \n",
    "cat_feats = training_input_data[['ocean_proximity']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f0e64e53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle missing values \n",
    "\n",
    "from sklearn.pipeline import Pipeline \n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipe = Pipeline([\n",
    "      ('imputer', SimpleImputer(strategy='mean')),\n",
    "      ('scaler', StandardScaler())\n",
    "\n",
    "    ])\n",
    "\n",
    "num_preprocessed = num_pipe.fit_transform(num_feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c5ff26fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pipeline to combine the numerical pipeline and also encode categorical features \n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# The transformer requires lists of features\n",
    "\n",
    "num_list = list(num_feats)\n",
    "cat_list = list(cat_feats)\n",
    "\n",
    "final_pipe = ColumnTransformer([\n",
    "    ('num', num_pipe, num_list),\n",
    "    ('cat', OneHotEncoder(), cat_list)\n",
    "])\n",
    "\n",
    "training_data_preprocessed = final_pipe.fit_transform(training_input_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f7c61f40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.67858615, -0.85796668,  0.97899282, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       [-0.93598814,  0.41242353,  0.18557502, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.45585107,  0.9187045 ,  0.02689146, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ...,\n",
       "       [ 1.27342931, -1.32674535,  0.18557502, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.64859406, -0.71733307,  0.97899282, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.44085502,  1.01246024,  1.37570172, ...,  0.        ,\n",
       "         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data_preprocessed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd496de6",
   "metadata": {},
   "source": [
    "<a name='5'></a>\n",
    "\n",
    "## 5 - Training Support Vector Regressor\n",
    "\n",
    "In regression, instead of separating the classes with decision boundary  like in classification, SVR fits the training data points on the boundary margin but keep them off. \n",
    "\n",
    "We can implement it quite easily with Scikit-Learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "869b31a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVR, SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "92959113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVR()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_svr = LinearSVR()\n",
    "lin_svr.fit(training_data_preprocessed, training_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b25ca83d",
   "metadata": {},
   "source": [
    "We can also use nonlinear SVM with a polynomial kernel function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "703253ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(kernel='poly')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_svr = SVR(kernel='poly')\n",
    "poly_svr.fit(training_data_preprocessed, training_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9afb9bde",
   "metadata": {},
   "source": [
    "<a name='6'></a>\n",
    "\n",
    "## 6 - Evaluating Support Vector Regressor\n",
    "\n",
    "Let us evaluate the two models we created on the training set before evaluating on the test set. This is a good practice. Since finding a good model can take many iterations of improvements, it's not advised to touch the test set until the model is good enough. Otherwise it would fail to make predictions on the new data. \n",
    "\n",
    "As always, we evaluate regression models with mean squarred error, but the commonly used one is root mean squarred error. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6dc728df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "215682.86713461558"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "predictions = lin_svr.predict(training_data_preprocessed)\n",
    "mse = mean_squared_error(training_labels, predictions)\n",
    "rmse = np.sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e79c6472",
   "metadata": {},
   "source": [
    "That's too high error. Let's see how SVR with polynomial kernel will perform. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b69c9f89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "117513.38828582528"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = poly_svr.predict(training_data_preprocessed)\n",
    "mse = mean_squared_error(training_labels, predictions)\n",
    "rmse = np.sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbdf054d",
   "metadata": {},
   "source": [
    "It did well than the former. For now, we can try to improve this with Randomized Search where we can find the best parameters. Let's do that!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b43c8d44",
   "metadata": {},
   "source": [
    "<a name='7'></a>\n",
    "\n",
    "## 7 - Improving Support Vector Regressor\n",
    "\n",
    "Let use Randomized Search to improve the SVR model. Few notes about the parameters:\n",
    "\n",
    "* *Gamma(y)*: This is a regularization hyperparameter. When gamma is small, the model can underfit. It is too high, model can overfit. \n",
    "* *C*: It's same as gamma. It is a regularization hyperparameter. When C is low, there is much regularization. When C is high, there is low regularization. \n",
    "* *epsilon*: is used to control the width of the street."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fd298880",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] END .............C=1, degree=2, epsilon=0, gamma=0.0001; total time=  13.7s\n",
      "[CV] END .............C=1, degree=2, epsilon=0, gamma=0.0001; total time=  13.5s\n",
      "[CV] END .............C=1, degree=2, epsilon=0, gamma=0.0001; total time=  13.6s\n",
      "[CV] END ................C=1, degree=2, epsilon=0, gamma=0.1; total time=  13.6s\n",
      "[CV] END ................C=1, degree=2, epsilon=0, gamma=0.1; total time=  13.6s\n",
      "[CV] END ................C=1, degree=2, epsilon=0, gamma=0.1; total time=  13.7s\n",
      "[CV] END ................C=1, degree=5, epsilon=0, gamma=0.1; total time=  13.6s\n",
      "[CV] END ................C=1, degree=5, epsilon=0, gamma=0.1; total time=  13.6s\n",
      "[CV] END ................C=1, degree=5, epsilon=0, gamma=0.1; total time=  13.6s\n",
      "[CV] END ........C=1000, degree=5, epsilon=0.5, gamma=0.0001; total time=  13.7s\n",
      "[CV] END ........C=1000, degree=5, epsilon=0.5, gamma=0.0001; total time=  13.7s\n",
      "[CV] END ........C=1000, degree=5, epsilon=0.5, gamma=0.0001; total time=  13.7s\n",
      "[CV] END .............C=1000, degree=5, epsilon=0, gamma=0.1; total time=  13.3s\n",
      "[CV] END .............C=1000, degree=5, epsilon=0, gamma=0.1; total time=  13.3s\n",
      "[CV] END .............C=1000, degree=5, epsilon=0, gamma=0.1; total time=  13.3s\n",
      "[CV] END ...........C=1000, degree=2, epsilon=0.5, gamma=0.1; total time=  13.2s\n",
      "[CV] END ...........C=1000, degree=2, epsilon=0.5, gamma=0.1; total time=  13.2s\n",
      "[CV] END ...........C=1000, degree=2, epsilon=0.5, gamma=0.1; total time=  13.3s\n",
      "[CV] END ..........C=1000, degree=2, epsilon=0, gamma=0.0001; total time=  13.8s\n",
      "[CV] END ..........C=1000, degree=2, epsilon=0, gamma=0.0001; total time=  13.7s\n",
      "[CV] END ..........C=1000, degree=2, epsilon=0, gamma=0.0001; total time=  13.8s\n",
      "[CV] END .............C=1000, degree=2, epsilon=0, gamma=0.1; total time=  13.1s\n",
      "[CV] END .............C=1000, degree=2, epsilon=0, gamma=0.1; total time=  13.2s\n",
      "[CV] END .............C=1000, degree=2, epsilon=0, gamma=0.1; total time=  13.2s\n",
      "[CV] END ...........C=1, degree=2, epsilon=0.5, gamma=0.0001; total time=  13.7s\n",
      "[CV] END ...........C=1, degree=2, epsilon=0.5, gamma=0.0001; total time=  13.7s\n",
      "[CV] END ...........C=1, degree=2, epsilon=0.5, gamma=0.0001; total time=  13.7s\n",
      "[CV] END ...........C=1000, degree=5, epsilon=0.5, gamma=0.1; total time=  13.2s\n",
      "[CV] END ...........C=1000, degree=5, epsilon=0.5, gamma=0.1; total time=  13.2s\n",
      "[CV] END ...........C=1000, degree=5, epsilon=0.5, gamma=0.1; total time=  13.2s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=SVR(),\n",
       "                   param_distributions={'C': [1, 1000], 'degree': [2, 5],\n",
       "                                        'epsilon': [0, 0.5],\n",
       "                                        'gamma': [0.0001, 0.1]},\n",
       "                   random_state=42, verbose=2)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "\n",
    "params = {'gamma':[0.0001, 0.1],'C':[1,1000], 'epsilon':[0,0.5], 'degree':[2,5]}\n",
    "\n",
    "rnd_search = RandomizedSearchCV(SVR(), params, n_iter=10, verbose=2, cv=3, random_state=42)\n",
    "\n",
    "rnd_search.fit(training_data_preprocessed, training_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8b4d8b0",
   "metadata": {},
   "source": [
    "We can now find the best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dcdbda1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gamma': 0.1, 'epsilon': 0, 'degree': 5, 'C': 1000}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "29f91afe",
   "metadata": {},
   "outputs": [],
   "source": [
    "svr_rnd = rnd_search.best_estimator_.fit(training_data_preprocessed, training_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3821f51c",
   "metadata": {},
   "source": [
    "Now, let's evaluate it on the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6e249653",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68684.15262765526"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = svr_rnd.predict(training_data_preprocessed)\n",
    "mse = mean_squared_error(training_labels, predictions)\n",
    "rmse = np.sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2fc0d66",
   "metadata": {},
   "source": [
    "Wow, this is much bettter. We were able to improve reduce the root mean squarred error from `$117,513` to `$68,684`. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "352268fd",
   "metadata": {},
   "source": [
    "Now, we can evaluate the model on the test set. We will have to transform it the same way we transformed the training set for the prediction to be possible. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "562f2117",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_input_data = test_data.drop('median_house_value', axis=1)\n",
    "test_labels = test_data['median_house_value']\n",
    "\n",
    "\n",
    "test_preprocessed = final_pipe.transform(test_input_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3ce32495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68478.07737338323"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred = svr_rnd.predict(test_preprocessed)\n",
    "test_mse = mean_squared_error(test_labels,test_pred)\n",
    "\n",
    "test_rmse = np.sqrt(test_mse)\n",
    "test_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9086f648",
   "metadata": {},
   "source": [
    "That's is not really bad."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53988b69",
   "metadata": {},
   "source": [
    "This is the end of the notebook. It was a practical introduction to using Support Vector Machines for regression. In the next lab, we will take a further step, where we will do classification with SVM. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03e25b66",
   "metadata": {},
   "source": [
    "### [BACK TO TOP](#0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee6a34fd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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